Aerial photography, light detection and ranging (“LIDAR”), synthetic aperture radar, and other types of remote sensing technologies are capable of capturing digital imagery for the purpose of extracting three-dimensional point coordinate data. These technologies are widely used in industry as vital tools to collect the data necessary for map-making, engineering, land management, and/or asset management. These tools are valuable because they can capture spatial (point coordinate) data in a digital format that ultimately allows a wide variety of computer-based tools to be applied to the tasks of map making, 3D modeling for engineering analysis, and/or land/asset management. Today, however, considerable time and effort (manual human intervention) is required to interpret the resulting imagery and extract information suitable (e.g., in a more meaningful object-oriented form) for input to existing computer-based tools for map-making, 3D modeling for engineering analysis, land management, and/or asset management. In order to input data directly from captured digital imagery into existing computer tools, the image must be interpreted and the objects included in the imagery must be extracted in an automated fashion (e.g. objects must be recognized and geometrically defined). Thus, systems and methods are continuously sought to improve the efficiency and accuracy of the image interpretation, object extraction, and data preparation processes related to map-making, engineering modeling, land management, and/or asset management tasks.
Three dimensional point coordinate data (3D imagery of a real-world scene) is practically useless unless individual points in space can be associated with recognizable objects within the scene, the physical and geometrical characteristics of the objects can be modeled, and the spatial relationships between the objects can be determined. The immediate need is to recognize objects within the scene. Next, the attributes (e.g., dimensions and physical characteristics) of the recognized object must be modeled. Finally, the objects must be analyzed to determine their relative spatial relationships. There are many tools available to model and analyze objects after they have been recognized and after their geometric and physical attributes have been determined. However, at the present time, there are only a few rudimentary approaches (usually based on simple data averaging or smoothing techniques or crude heuristic rules) for “filtering” or classifying three-dimensional point data to determine which subset of the points might be associated with a particular type of only a very few recognizable objects.
One approach is to have photogrammetrists construct stereo-models from pairs of stereo photographs, either traditional film or scanned digital images. Photogrammetrists then use their experience to interpret the images and manually digitize the geometric characteristics of the objects they are able to recognize. Another approach is to have photogrammetrists and/or scientists construct models by geo-referencing LIDAR and/or synthetic aperture radar 3D point data to digital aerial photography or satellite imagery. They then use their experience to interpret the images. They either manually digitize the geometric characteristics of the objects they are able to recognize or use semi-automated computer tools to select individual 3D coordinate points that represent the objects that were recognized. Also, drafters and/or engineers may construct various 2D projections of 3D digital imagery data using CAD (Computer-Aided Drafting) systems and attempt to visualize the data. They manually select or discard points in order to construct a model from the imagery. Technicians may color code strata within the digital imagery by assigning different colors to designate elevation bands in hopes that topographic features can be recognized and their geometric characteristics can be digitized. Such visualization techniques may not be able to produce repeatable results.
Alternatively, mathematicians and programmers may use moving averages of point elevations (considered along scan-lines) and other basic mathematical techniques or heuristic rules to analyze 3D point data and attempt to classify points that belong to the ground surface or other fundamental features or classes of objects within the imagery data.
Each example of these solutions above is directed toward the tasks involved with defining the “bare-earth” or “ground” surface and then discerning other recognizable objects within the field of the image so that the objects' geometric and other physical attributes can be digitized and the spatial relationships between objects can be modeled for subsequent analysis (e.g., map-making, engineering, land management, and/or asset management).
However, all of the above methods have major shortcomings. First, such methods produce inadequate and inaccurate results, especially when considering relatively sparse point data density. In addition, such methods are lengthy, time-consuming manual and/or semi-automated procedures. Since such procedures require a preservation of the original step-by-step process and intermediate results, only incremental improvements in operator productivity and product throughput can be achieved even with increases in operator experience. Moreover, such procedures often require specialized computing hardware and/or software. Also, such procedures produce results that either cannot be repeated or in most cases cannot be repeated with any degree of confidence. Often this is due to a requirement for significant experience or training of the human operator to interpret the intermediate results before the final result can be obtained, or it is due to a requirement for rigorous endurance on the part of a human operator in a tedious work environment. Such difficulties are compounded by the need for the production of multiple intermediate products before the final results can be achieved.
Thus, there is a need for a system to automate the rapid and accurate production of topographic/terrestrial features and/or surface models (whether of paths/corridors or areas) from large volumes of apparently random three-dimensional digital imagery coordinate point data acquired from synthetic aperture radar, LIDAR, acoustic and/or other similar technologies. There is a further need for a system which can break down the digital imagery (e.g., the three-dimensional coordinate point set) into relatively simple local structures that can be recognized based on mathematical characteristics derived from the relative spatial relationships between the individual points in the image and then extract or compose globally complex structures as assembled from collections of like/similar local structures.